{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:L6MXLM6TCWSECBNNCRJGGEMVNQ","short_pith_number":"pith:L6MXLM6T","schema_version":"1.0","canonical_sha256":"5f9975b3d315a44105ad14526311956c34c416bf502d7fbc7cc6d44af2addaec","source":{"kind":"arxiv","id":"2606.01428","version":1},"attestation_state":"computed","paper":{"title":"Quantifying Evidential Rigor in Meta-Analytic Corpora: A Simulation-Characterized, Bias-Robust Bayesian Workflow with a Nutrition Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Matt Hester","submitted_at":"2026-05-31T19:56:12Z","abstract_excerpt":"Conventional meta-analysis summarizes evidence through pooled estimates, intervals, and p-values, but these outputs do not directly measure evidence for an effect, evidence for no effect, or the degree to which conclusions depend on publication selection or small-study effects. We introduce a corpus-scale Bayesian evidential-audit workflow for meta-analytic corpora. The workflow reconstructs or accepts study-level effects and standard errors, harmonizes directions, fits a matched Bayesian random-effects baseline and a bias-aware model-averaged ensemble, and reports paired estimates with compon"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.01428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-31T19:56:12Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"a2edd2642f94521d5037979f5beceb56901dd229747b83e8dd919801e3acd807","abstract_canon_sha256":"d2d2beff65fd3b6c0daf56b009179d5b9462027f38eb31186112056e9df9f7cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:32.954591Z","signature_b64":"nkOb430/pGfwNSGlT2Vf+ksk8l692P4LbWqmbH4fmdQTrfiHGACaE0muR7SXqjoDOWdupuNEDBbWZLmvChUQBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f9975b3d315a44105ad14526311956c34c416bf502d7fbc7cc6d44af2addaec","last_reissued_at":"2026-06-02T02:04:32.954225Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:32.954225Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantifying Evidential Rigor in Meta-Analytic Corpora: A Simulation-Characterized, Bias-Robust Bayesian Workflow with a Nutrition Case Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Matt Hester","submitted_at":"2026-05-31T19:56:12Z","abstract_excerpt":"Conventional meta-analysis summarizes evidence through pooled estimates, intervals, and p-values, but these outputs do not directly measure evidence for an effect, evidence for no effect, or the degree to which conclusions depend on publication selection or small-study effects. We introduce a corpus-scale Bayesian evidential-audit workflow for meta-analytic corpora. The workflow reconstructs or accepts study-level effects and standard errors, harmonizes directions, fits a matched Bayesian random-effects baseline and a bias-aware model-averaged ensemble, and reports paired estimates with compon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.01428","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.01428/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.01428","created_at":"2026-06-02T02:04:32.954287+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.01428v1","created_at":"2026-06-02T02:04:32.954287+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.01428","created_at":"2026-06-02T02:04:32.954287+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6MXLM6TCWSE","created_at":"2026-06-02T02:04:32.954287+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6MXLM6TCWSECBNN","created_at":"2026-06-02T02:04:32.954287+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6MXLM6T","created_at":"2026-06-02T02:04:32.954287+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ","json":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ.json","graph_json":"https://pith.science/api/pith-number/L6MXLM6TCWSECBNNCRJGGEMVNQ/graph.json","events_json":"https://pith.science/api/pith-number/L6MXLM6TCWSECBNNCRJGGEMVNQ/events.json","paper":"https://pith.science/paper/L6MXLM6T"},"agent_actions":{"view_html":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ","download_json":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ.json","view_paper":"https://pith.science/paper/L6MXLM6T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.01428&json=true","fetch_graph":"https://pith.science/api/pith-number/L6MXLM6TCWSECBNNCRJGGEMVNQ/graph.json","fetch_events":"https://pith.science/api/pith-number/L6MXLM6TCWSECBNNCRJGGEMVNQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ/action/storage_attestation","attest_author":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ/action/author_attestation","sign_citation":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ/action/citation_signature","submit_replication":"https://pith.science/pith/L6MXLM6TCWSECBNNCRJGGEMVNQ/action/replication_record"}},"created_at":"2026-06-02T02:04:32.954287+00:00","updated_at":"2026-06-02T02:04:32.954287+00:00"}